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We’re building our AGI discussion around a few core papers. The ones identified here are either central to our discussion – and will be featured across multiple YouTubes and blogposts (and also front-row in our AGI 101 Themesis course), or are hugely and supremely supportive of our studies. This blogpost specifically correlates with THIS YOUTUBE:… Continue reading AGI Basics: Five Key Reads

This is a blogpost-in-progress. It is intended to provide resources accompanying a YouTube that I plan to put out this week. Check back for more as the week progresses – thank you! – AJM (Monday, May 6th, 2024; 1AM Hawai’i time) Resources and References From the Prologue to LeCun’s work: This document is not a… Continue reading AGI: Fundamental Concepts (Getting Started Reading List)

Four years ago, tech journalist and old friend Lee Goldberg asked me to semi-/sort-of collaborate with him on his yearly April Fool’s (April 1st) day article. Basically, Lee had a really fun idea for writing about “artificial stupidity,” instead of “artificial intelligence,” and he asked me to give him some real-AI credibility to use in… Continue reading Generative AI: A Teenager Acting Out

First, Make Sure that You Understand Discriminative Neural Networks Discriminative neural networks are the kinds of neural networks where you train the network with a known and pre-labeled training and testing data set. Essentially, you “have the answers in the back of the book” – and you train the network by constantly having it check… Continue reading Your Generative AI Self-Study Plan

Teaching yourself generative AI (“gen-AI”) has to be one of the hardest things in the world. The really important, classic papers – the ones that you WISH THAT YOU COULD READ – all presuppose that you have a lot of knowledge coming in, about all SORTS of things. The situation is the same as it… Continue reading If You’re Teaching Yourself Generative AI: Some Resources (Book Chapters)

In order to read any of the classic (and important) papers on energy-based neural networks, we need to know the vocabulary and essential concepts from: In today’s associated YouTube video, we illustrate how these different terms – and their respective disciplines – are blended together, using the Salakhutdinov and Hinton (2012) paper as a reference… Continue reading Learning Energy-Based Neural Networks

Socrates is a name that most of us know, even if only tangentially. We’ve probably all heard of the “Socratic method” of teaching. Many of us also know of Pericles, the great Athenian general who lead their armies in their various wars – including those against Sparta. But Aspasia? Not as many of us have… Continue reading Socrates, Pericles, and Aspasia: How A Muse Helped the Genesis of Western Thought

This is the blogpost associated with THIS YOUTUBE: The BEST “Readings” Resources to go with this YouTube are TWO prior blogposts: Also, the prior neurophysiology-oriented YouTube has a LOT of useful content – it’s all on how the neurophysiology that we can use as inspiration has evolved over these past 50 ++ years. So the… Continue reading Neurophysiology-Based AGI Framework in 2024

One of the most important things that we can do, in creating AGI (artificial general intelligence), is to work through the latent variable issues that are foremost in AI and machine learning (ML) research now. We identified these in our July 10, 2023 blogpost on Latent Variables in Neural Networks and Machine Learning. We wrote… Continue reading The AI Salon: AGI and Latent Variables

The most important element in creating an AGI (artificial general intelligence) is that the latent node layer needs to allow a range of neural dynamics. The most important of these dynamics will be the ability for the system to rapidly undergo a state change, from mostly “off” nodes to mostly “on.” Neurophysiologists have observed this… Continue reading The 1D CVM (Cluster Variation Method): Complete Interactive Code (Part 2)